Reduction of a district heating model using network decomposition

Abstract

AbstractIn this contribution we study model order reduction of nonlinear transport networks for district heating systems. In these, heating energy injected at a centralized power plant is transported to consumers. They are modeled by a hyperbolic differential algebraic system with large state space dimension. The network structure introduces sparse system dynamics, which transform to a dense reduced system leading to unacceptable computational costs [1]. To exploit the benefits of sparsity, sub‐parts of the network are reduced separately in a structure preserving way using Galerkin projection [2]. After introducing the dynamical model, we discuss a decomposition strategy which aims at minimizing the number of observables for each subnetwork. We demonstrate its numerical benefits at an existing large scale heating network.